Barnes–Hut approximation based accelerating t-SNE for seizure detection

Automatic detection of epileptic seizures is critical in the paradigm of epilepsy diagnosis and in relieving the cumbersome visual inspection of electroencephalogram (EEG) recordings. A speedy algorithm could help in more reliable monitoring and detection of seizures. In this study, we aim to provide an EEG-based seizure detection system with computational efficiency and improved performance. In the proposed work, many features including temporal, spectral, and non-linear features from each intrinsic mode function (IMF) of empirical mode decomposition (EMD) have been used. Barnes–Hut approximation-based t-stochastic neighborhood embedding (bh t-SNE) was explored for the first time to observe the reduction in computational time (CT) period in the automatic seizure detection system. Three classes of widely-used EEG Bonn datasets were used to assess the performance of the proposed method. The proposed Barnes–Hut-based accelerating t-SNE along with SVM and KNN reduced more than half of the classification time with the same accuracy. The classifier takes 2.147±0.1 s for SVM and 1.216±0.1 s for KNN without the proposed t-SNE and 1.31±0.1 s for SVM and 0.736±0.1 s for KNN (at the trade-off parameter θ=0.5) with the proposed Barnes–hut based t-SNE (bh t-SNE) at an accuracy of 100%. Conclusions: The findings of the experimental work indicate that the proposed method is effective in reducing the computational time while maintaining the required efficacy. As a result, the inclusion of these algorithms in hardware might prove to be effective in assisting neurologists in detecting seizures.